http://hdl.handle.net/11189/1830 2026-11-12T15:37:23Z 2026-11-12T15:37:23Z Nxumalo, Yolanda Dolly balyan, vipin Gupta Gunjan http://hdl.handle.net/11189/11006 2026-11-10T20:46:46Z 2025-01-01T00:00:00Z

Title: Energy harvesting in LoRaWAN for space communication Authors: Nxumalo, Yolanda Dolly; balyan, vipin; Gupta Gunjan Abstract: LoRa’s low-power, long-range capabilities enhance data collection and monitoring in Internet of Things (IoT) and remote sensing across vast areas, driving its widespread adoption in industries like asset tracking, environmental monitoring, agriculture, and healthcare, especially in remote and infrastructure-constrained regions. Applications include soil moisture monitoring, wildlife tracking, and precision agriculture. However, terrestrial LoRa communication has coverage limitations due to line-of-sight constraints wherein sparse population density or complex terrain might block or reflect the signal. In addition, the devices require recurrent recharging or battery replacement which poses significant operational challenges and increases costs. To address these challenges, satellite-based LoRa communication emerges as a potential solution for global coverage. However, robust power management techniques are required due to the power requirements of direct-to-satellite communication and the challenging environmental conditions that are frequently encountered in remote regions. This research introduces a novel Ambient Energy Management (AEM) system designed for energy harvesting to power LoRa Internet of Things (IoT) devices operating in a Direct-to-Satellite (DtS) communication environment. This study explores how duty cycle, signal design, and energy harvesting techniques interact while tackling the difficulties of energy optimization in this setting. The objective of the research is to increase energy efficiency, prolong system lifespan, and broaden the possible uses of LoRa-based IoT systems while complying with regional regulatory standards, including those described by Independent Communication Authority of South (ICASA). This study contributes to the advancement of energy-autonomous IoT systems by introducing novel algorithms and insights into the management of harvested energy for satellite communication.

2025-01-01T00:00:00Z Van den Berg, Carolien Verster, Belinda http://hdl.handle.net/11189/10991 2026-11-03T20:45:41Z 2025-01-01T00:00:00Z

Title: Bridging local realities and global goals: exploring student-led sustainable-smart innovation projects in a marginalised community Authors: Van den Berg, Carolien; Verster, Belinda Abstract: This study explores the intersection of Higher Education for Sustainable Development (HEfSD), sustainable-smart innovation and the significance of local context in digital technology design, as captured in student-led projects within a marginalised community in Cape Town. As the global imperative to achieve the Sustainable Development Goals (SDGs) intensifies, integrating sustainable practices and innovative technologies at the local and community scale becomes paramount. This research investigates the potential of collaborative educational approaches in addressing complex sustainability challenges through the lens of a specific Cape Town community. The paper provides findings from the third iteration of a longitudinal Design-Based Research (DBR) study. Data includes interviews, participatory observations, and project artefacts. This research seeks to identify the enablers and barriers to validate an additional design principle for the implementation of sustainable-smart student projects. It examines the collaborative design thinking process, interdisciplinary approaches, and community engagement employed by students and educators to co-create sustainable-smart innovations that respond to both local challenges and global sustainability goals. By doing so, this study sheds light on the potential of HEfSD to nurture sustainable-smart innovation within unique socio-economic and cultural contexts. The research underscores the need for higher education institutions to embrace localised, student-centred initiatives that harness the power of innovation to drive positive change in marginalised communities, thus advancing both sustainability and social equity agendas.

2025-01-01T00:00:00Z Court, Evan Balyan, Vipin http://hdl.handle.net/11189/10514 2026-01-26T21:26:33Z 2024-01-01T00:00:00Z

Title: A high efficiency class-E power amplifier with tuned output network Authors: Court, Evan; Balyan, Vipin Abstract: A switch-mode power amplifier (PA), like the proposed class-E renders itself capable of achieving a theoretical power conversion efficiency (PE) of 100% by ensuring specific bias and switching conditions of the active device and presenting an idealized impedance at the drain of the active device [1] [2] [3]. The design presented satisfies the zero-voltage switching (ZVS) and zero derivative voltage switching (ZVDS) conditions which are fundamental to correct class-E PA operation. The output network performs a dual function of impedance transformation to a standard 50 ohm impedance and harmonic suppression for any power being transmitted at harmonic frequencies.

2024-01-01T00:00:00Z Adewuyi, Oludamilare Bode Krishnarmurthy, Senthil http://hdl.handle.net/11189/10513 2026-01-26T21:24:18Z 2023-01-01T00:00:00Z

Title: Performance assessment of steady-state voltage stability indices for parameter validation using ANFIS Authors: Adewuyi, Oludamilare Bode; Krishnarmurthy, Senthil Abstract: Precision and consistency in monitoring the voltage stability conditions of power system networks remain vital aspects of modern power system operation. Thus, this study presents a machine learning approach for comparative performance evaluation of some voltage stability indices (VSIs). Six essential power system parameters from the transmission line data and load flow solution at different real and reactive load levels are used as input information for implementing adaptive neuro-fuzzy inference system (ANFIS) models based on the subtractive clustering rules. The performance of the considered VSIs is evaluated using the mean absolute percentage error (MAPE), the Percentage Relative Root-Mean-Square Error (RRMSEp), and the linear regression based on correlation coefficient R. The IEEE 30-bus system is used as a test, and the predictive analysis results are obtained for several simulation runs for parameter tuning and model regularity. Considering the MAPE analysis, FVSI with average MAPE = 4.7429% and maximum MAPE = 6.6574% outperformed the other VSIs; average MAPE values are 5.6165% for LMN, 12.4684% for NLSI and 14.7167% for LQP. The consistency of FVSI for precise monitoring of power system voltage stability condition, compared to the other VSIs, is verified by the RRMSEp and R analysis results.

2023-01-01T00:00:00Z